Intro to ggplotly()

library(plotly)

nfl_qbr_plot <- nfl_qbr %>% 
  ggplot(aes(x = total_epa, y = qbr_total, label = short_name)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_label() +
  theme_minimal() +
  labs(
    x = "EPA", y = "QBR", 
    title = "EPA is correlated with QBR"
    )

nfl_qbr_plot

ggplotly(nfl_qbr_plot)

Intro to plot_ly()

• Any plot made with plot_ly() uses the JavaScript library plotly.js

• plot_ly() interfaced directly with plotly.js

• plot_ly has arguments that fit into the “Grammar of Graphics”:

  • x, y
  • color, stroke, span, symbol, linetype

• There is a family of add_* functions:

  • add_lines()
  • add_bars
  • add_histogram2d()
  • add_contour
  • add_boxplot
  • …and many more!

The plotly package takes a pure functional approach to a layered grammar of graphics, meaning (almost) every function anticipates a plotly object as input to it’s first argument and returns a modified version of that plotly object.

Example: the layout() function anticipates a plotly object in it’s first argument and its other arguments add and/or modify various layout components of that object (e.g., the title):

layout(
  plot_ly(nfl_qbr %>% filter(game_week == 3), x = ~short_name, y = ~total_epa, color = ~rank),
  title = "QB Total EPA for Week 3"
)

Notice that data manipulation verbs from the dplyr package (such as filter()) can be used to transform the data underlying a plotly object!

Or, in a more cleaner fashion:

nfl_qbr %>% 
  filter(game_week == 3) %>% 
  plot_ly(x = ~short_name, y = ~total_epa, color = ~rank) %>% 
  add_bars() 

You can also add a layer of text using the summarized counts. Note that the global x mapping, as well as the other mappings local to this text layer (text and y), reflect data values from step 3:

nfl_qbr %>% 
  filter(game_week == 3) %>% 
  plot_ly(x = ~short_name, y = ~total_epa, color = ~rank) %>% 
  add_bars() %>% 
  add_text(
    text = ~team,
    textposition = "top middle",
    cliponaxis = F
  )

To recap:

1. Globally assign short_name,total_epa, rank to x, y and color, respectively
2. Add a bars layer (which inherits the y from plot_ly)
3. Use dplyr verbs to modify the data underlying the plotly object
4. Add additional layers (like a layer of text)
5. Share

D3

The amazing dreamRs team is behind the {r2d3maps} package which allows you to make D3 maps in a flash. Check out their documentation to get a taste of what you can make: https://github.com/dreamRs/r2d3maps.

# Packages ----------------------------------------------------------------
library(janitor)
library(albersusa)
library(sf)
library(tigris)
library(r2d3maps)
library(rmapshaper)

# County geometries -------------------------------------------------------

cty_sf <- counties_sf("longlat") # from albersusa package 
plot(st_geometry(cty_sf)) # checking geometries, looks good

cty_sf <- ms_simplify(cty_sf) # make the geometries simpler for faster rendering 


# Data ---------------------------------------------------------------------

# CDC's social vulnerability index variables: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf

svi <- read_csv("/Users/atoumi/Desktop/interactive_graphics/svi_county.csv") %>% 
  clean_names() %>% # make everything snake_case
  filter(across(where(is.numeric), ~. >= 0)) # because NA's are coded as -Inf

# join data to geometries
cty_sf_joined <- cty_sf %>% geo_join(svi, by_sp = "fips", by_df = "fips")

# make d3 map
d3_map(shape = cty_sf_joined, projection = "Albers") %>%
  add_labs(caption = "Viz: Asmae Toumi | Data: CDC") %>% 
  add_continuous_breaks(var = "rpl_theme1", palette = "Reds") %>%
  add_legend(title = "Socioeconomic Vulnerability (percentile)") %>%
  add_tooltip("<b>{location}</b>: {rpl_theme1}")

Leaflet

Like plotly, leaflet has an R interface. Unlike D3, Leaflet is more suited for maps. Leaflet is well-suited for the web, highly customizable and integrates well with R’s ecosystem.

library(tigris)

cty <- counties(cb = TRUE, resolution = "20m") %>% rename(fips = GEOID)

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library(tidycensus)

us_county_income <- 
  get_acs(geography = "county", variables = "B19013_001") %>% 
  select(fips = GEOID, name = NAME, income = "estimate") %>% 
  drop_na()

cty <- cty %>% 
  geo_join(us_county_income, by_df = "fips", by_sp = "fips")

library(leaflet)
library(RColorBrewer)
library(htmltools)

pal <- colorNumeric("YlOrRd", domain = cty$income)

labels <- 
  sprintf(
    "<strong>%s</strong><br/> Income: %s $",
    cty$name, cty$income) %>% 
  lapply(htmltools::HTML)

leaflet(cty) %>%
  setView(-96, 37.8, 4) %>%
  addProviderTiles("CartoDB.PositronNoLabels") %>%
  addPolygons(
    fillColor = ~pal(income), 
    weight = 1,
    opacity = 1,
    color = "white",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 2,
      color = "#666",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(pal = pal,
            values = cty$income,
            position = "bottomright",
            title = "Income (5-year ACS)",
            labFormat = labelFormat(suffix = "$"),
            opacity = 0.8,
            na.label = "No data")

See leaflet.R for standalone code.

Saving plotly/ggplotly objects

• Plotly/ggplotly objects are also htmlwidgets so any methods used for saving htmlwidgets also works for plotly/ggplotly objects:
  • Saving and embedding HTML: htmlwidgets::saveWidget() function (also works for leaflet, DT, etc.)
  • Exporting static images: orca() as .svg or .pdf

Combining multiple views

p1 <- plot_ly(nfl_qbr, x = ~total_epa, y = ~qbr_total) %>% 
  add_lines(name = "qbr total")

p2 <- plot_ly(nfl_qbr, x = ~total_epa, y = ~qb_plays) %>% 
  add_lines(name = "qb plays")

subplot(p1, p2, shareX = T)

gg <- nfl_qbr %>%
  filter(team %in% c("ARI", "ATL", "CHI", "CAR", "DAL")) %>% 
  ggplot(aes(x = total_epa, y = qbr_total, color = team)) +
  geom_point(aes(frame = game_week)) 

ggplotly(gg)

Arranging multiple views

Since plotly objects are also htmlwidgets, any method that works for arranging htmlwidgets also works for plotly objects.

Common ways to arrange components in a single web-page:

1. flexdashboard: An R package for arranging components into an opinionated dashboard layout. This package is essentially a special rmarkdown template that uses a simple markup syntax to define the layout.

2. Bootstrap’s grid layout: Both the crosstalk and shiny packages provide ways to arrange numerous components via Bootstrap’s (a popular HTML/CSS framework) grid layout system.

3. CSS flexbox: If you know some HTML and CSS, you can leverage CSS flexbox to arrange components via the htmltools package.

We’ll see how crosstalk, flexdashboard and DT work together with Demo.Rmd script. We’ll also see a Shiny app with the app.R script.

For more on shiny, check out this talk by Carson Sievert: https://talks.cpsievert.me/20191115/#1